Server-supported malware detection and protection

ABSTRACT

A processor-readable storage device storing instructions that cause a processor to perform operations including, subsequent to determining, at a first device based on a first file attribute associated with a file, that a classification for the file is unavailable at the first device, sending the first file attribute from the first device to a second device to determine whether the classification for the file is available at the second device. The operations include receiving a notification at the first device from the second device that the classification for the file is unavailable at the second device. The operations include, determining the classification for the file by performing, at the first device, an analysis of a second file attribute based on a trained file classification model. The operations include sending the classification from the first device to the second device and to a third device.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority from and is a continuation application of U.S. patent application Ser. No. 16/406,284, entitled “SERVER-SUPPORTED MALWARE DETECTION AND PROTECTION,” filed May 8, 2019, which claims priority from and is a continuation application of U.S. patent application Ser. No. 15/639,520 now issued as U.S. Pat. No. 10,305,923, entitled “SERVER-SUPPORTED MALWARE DETECTION AND PROTECTION,” filed Jun. 30, 2017, both of which are incorporated herein by reference in their entirety.

BACKGROUND

Computing devices have increasingly become targets of malicious actors through use of malware. Some types of malware, such as viruses, can cause loss of important business or personal data. Other types of malware, such as spyware, can enable malicious actors to gain access to confidential information.

Many malware detection applications use signatures (or malware definitions) to detect malware. For example, an executable file that includes malware may be processed using a hash function to generate a hash value. The hash value may act as a signature of the entire executable file or of a malware portion of the executable file. The signature may be provided to users of a malware detection application via a malware definitions update. During operation, the malware detection application compares a signature of a malware application in the malware definitions to files in memory or to received files to determine whether the files include the malware. One problem with detecting malware in this way is that new malware can be generated very quickly, potentially at a rate that is equal to or greater than distribution of new malware definitions. Accordingly, it is not unusual for a new malware or new malware-containing file to be distributed to many consumers before the consumers receive appropriate malware definitions updates to enable detection of the new malware.

SUMMARY

Particular implementations of systems for detecting malware using machine learning processes are described herein. For example, a set of malware containing files and a set of non-malware containing files may be used as training data to build a classifier (e.g., a data model configured to classify a file as malware containing or non-malware containing based on non-linear analysis of features of the file). The classifier (or “file classifier”) may include a decision tree, a support vector machine, a deep neural network, or another type of trained data model (or application that executes based on a data model) to detect malware. For example, the classifier may include a data structure that describes a decision tree data model, where the decision tree data model includes one or more interior nodes, leaf nodes, and edges. In this example, the configuration of the decision tree is trained (e.g., using a gradient decent process), based on features associated with training data (e.g., the set of malware containing files, the set of non-malware containing files, and classification information associated with each file) to identify patterns in the features that correspond to malware and patterns in the features that correspond to non-malware. Subsequently, to determine whether a new file includes malware, features are extracted from the new file and provided as input to the classifier. The classifier generates a result indicating that the file likely includes malware (e.g., due to the features of the new file resembling the features of malware containing files used to train the classifier) or a result indicating that the file likely does not include malware (e.g., due to the features of the new file resembling the features of non-malware containing files used to train the classifier).

In a particular example disclosed herein, the classifier may be trained using features derived from binary files. Binary files may be executable files or files that contain executable portions in a binary form. Binary files may be processed to generate features by converting the binary files into feature data. The feature data may include a set of characters (e.g., printable characters or entropy indicators) based on the binary files, as described further below. The feature data may be used to generate one or more vectors that are provided as input (e.g., supervised training data) to train a file classifier to determine whether or not particular files contain malware.

In some implementations, the supervised training data may indicate particular types of malware associated with the malware containing files. In such implementations, the file classifier may be trained to distinguish malware containing files from non-malware containing files, and to distinguish the different types of malware within the malware containing files. For example, the file classifier may be trained to indicate that a particular file is free of malware, contains a virus, contains a worm, contains a trojan, contains spyware, contains ransomware, contains adware, etc.

One advantage of using a file classifier rather than signature data is that the file classifier may be able to identify malware that has not been specifically used to train the file classifier. For example, a trained file classifier may be able to identify files that contain so called “zero day” malware, for which no malware definition is yet available. Another advantage is that the file classifier may occupy a relatively a smaller portion of memory than a large set of malware definitions. Further, scanning a file by generating a hash of the file and comparing the hash to every hash in a set of malware definitions may take longer than providing features representing the file to the classifier and generating a classification result. Further, the file classifier can be combined with other approaches to provide more robust malware protection. For example, a hash can be generated of a new file and compared to hash values of known good (e.g., non-malware containing) files, to hash values of known bad (e.g., malware containing) files, or both, to determine whether the new file contains malware. Regardless of whether the comparison of hash values indicates that the new file contains malware, features may be extracted from the new file and provided to the file classifier to generate a classification result indicating whether the new file contains malware. Accordingly, in this example, the file classifier-based process and the signature-based process provide complementary and substantially independent layers of malware detection, which may improve the likelihood of detecting malware containing files.

In some aspects, an endpoint security software product operating in accordance with the present disclosure may be installed on desktops, laptops, mobile devices, etc. Rather than traditional signature-based malware detection, the software product may utilize artificial intelligence. In particular, the software product may utilize a model that is trained on a large set of files, including benign files and malicious files. When an endpoint device executing the software product encounters a file, the device extracts a feature vector from the file and send the feature vector to a network server. The network server, which stores or otherwise has access to the model, may run the model on the feature vector to determine whether the file encountered by the device is classified as benign or malicious. The network server provides the classification decision to the device. However, as the number of devices executing the endpoint security software product increases, a single network server may not have the capacity to server all file classification requests.

Thus, in some examples, to implement load balancing and per-enterprise policies, the server-side framework for the software product may be implemented as a multi-level hierarchical system. To illustrate, a top level of the hierarchical system may include one or more “master” servers that store or otherwise have access to a “base” classification model and a “base” prediction cache. The base classification model may be trained using known benign files and malicious files, and the base prediction cache may store benign/malicious classifications of files that have been encountered recently. A second level of the hierarchical system may include various “enterprise” servers that are specific to individual enterprises (e.g., business entities, government organizations, etc.) or to individual consumers (which may be associated with one or more users). Each such enterprise server may be a part of a private network/subnet associated with the corresponding enterprise. Some enterprise servers may store or access an enterprise-specific classification model and/or prediction cache that differs from the “base” versions available at the master servers. For example, an enterprise-specific file classification model may be created for a chain of retail stores by subjecting the base file classification model to additional training iterations based on known malware and/or benign files that were used to train the base file classification model but are predicted to be more frequently encountered by endpoint devices operated by retail store employees or located in retail spaces (e.g., point-of-sale devices). As used herein, an “enterprise-specific” model refers to a model whose training set and/or training methodology is differentiated from a training set and/or training methodology of a “base” model in view of at least one characteristic of the enterprise.

When an endpoint device (also referred to herein as a “client” device) encounters a file, the endpoint device requests its enterprise server for a benign/malicious classification for the file. If the file has not previously been encountered by any endpoint devices at the enterprise, then the enterprise server may not have a prediction for the file available in its prediction cache. Consequently, the enterprise server may pass the classification request upstream to a master server. Here again, if the master server has not previously seen the file, a classification may be unavailable in the base prediction cache. At this point, the enterprise server may run its classification model on the file to determine whether the file is classified as malicious or benign. The classification may be passed downstream to the endpoint device and may be stored in the enterprise server's prediction cache. Furthermore, the classification and details regarding the file may be passed upstream to a master server for storage in the base prediction cache.

By storing a record of the newly encountered file and its classification at the top level of the hierarchical system, the hierarchical system may provide near-instant inoculation to newly detected (e.g., zero-day) malware threats. That is, a file encountered by an endpoint device at one enterprise can result in near-immediate knowledge and protection for endpoint devices at that enterprise and at other enterprises. To illustrate, if an endpoint device at a different enterprise encounters the same file, then when the enterprise server at that enterprise requests the master server to check the base prediction cache, a cache hit occurs because the file was previously classified. Moreover, the next time the base classification model is updated and enterprise-specific classification models are generated, the file may be part of the training set.

In a particular aspect, a method includes receiving, at a server (e.g., an enterprise server), a first file attribute from a computing device (e.g., an endpoint device), the first file attribute associated with a file. The method also includes determining, based on the first file attribute, that a classification for the file is unavailable. The method further includes determining the classification for the file based on a trained file classification model accessible to the server and sending the classification to the computing device. The method includes sending at least the classification to a base prediction cache associated with a second server (e.g., a master server).

In another particular aspect, a method includes receiving, at a first server (e.g., a master server) from a second server (e.g., a first enterprise server) that is associated with a first group of computing devices, a first file attribute of a first file and a classification for the first file. The method also includes adding the first file attribute and the classification to a base prediction cache. The method further includes receiving, at the first server from a third server (e.g., a second enterprise server) that is associated with a second group of computing devices, a second file attribute of a second file. The method includes determining, based on the base prediction cache, that the second file attribute matches the first file attribute. The method also includes, in response to determining that the second file attribute matches the first file attribute, sending, from the first server to the third server, the classification for the first file as a classification for the second file.

In another particular aspect, a server includes a memory storing instructions and a processor configured to execute the instructions from the memory to perform operations including receiving a first file attribute from a computing device. The first file attribute is associated with a file. The operations also include determining, based on the first file attribute, that a classification for the file is unavailable. The operations further include determining the classification for the file based on a trained file classification model accessible to the server. The operations include initiating sending the classification to the computing device and initiating sending at least the classification to a base prediction cache associated with a second server.

In another particular aspect, a server includes a memory storing instructions and a processor configured to execute the instructions from the memory to perform operations including receiving, from a second server that is associated with a first group of computing devices, a first file attribute of a first file and a classification for the first file. The operations also include adding the first file attribute and the classification to a base prediction cache. The operations further include receiving, from a third server that is associated with a second group of computing devices, a second file attribute of a second file. The operations include determining, based on the base prediction cache, that the second file attribute matches the first file attribute. The operations also include, in response to determining that the second file attribute matches the first file attribute, initiating sending, to the third server, the classification for the first file as a classification for the second file.

In another particular aspect, a processor-readable storage device stores instructions that, when executed, cause a processor to perform operations including receiving, at a server, a first file attribute from a computing device. The first file attribute is associated with a file. The operations also include determining, based on the first file attribute, that a classification for the file is unavailable. The operations further include determining the classification for the file based on a trained file classification model accessible to the server and initiating sending the classification to the computing device. The operations include initiating sending at least the classification to a base prediction cache associated with a second server.

In another particular aspect, a processor-readable storage device stores instructions that, when executed, cause a processor to perform operations including receiving, at a first server from a second server that is associated with a first group of computing devices, a first file attribute of a first file and a classification for the first file. The operations also include adding the first file attribute and the classification to a base prediction cache. The operations further include receiving, at the first server from a third server that is associated with a second group of computing devices, a second file attribute of a second file. The operations include determining, based on the base prediction cache, that the second file attribute matches the first file attribute. The operations also include, in response to determining that the second file attribute matches the first file attribute, initiating sending, from the first server to the third server, the classification for the first file as a classification for the second file.

In a particular aspect, a computing device includes a memory configured to store instructions to execute a file classifier and a processor configured to execute the instructions from the memory. Execution of the instructions causes the processor to perform operations including receiving one or more n-gram vectors for a file as input to the file classifier, where the one or more n-gram vectors indicate occurrences of character pairs in printable characters representing the file. The operations also include generating, by the file classifier, output including classification data associated with the file based on the one or more n-gram vectors, where the classification data indicates whether the file includes malware.

In another particular aspect, a method includes receiving one or more n-gram vectors for a file as input to a file classifier, where the one or more n-gram vectors indicate occurrences of character pairs in printable characters representing the file. The method also includes generating, by the file classifier, output including classification data associated with the file based on the one or more n-gram vectors, where the classification data indicates whether the file includes malware.

In another particular aspect, a computer-readable storage device stores instructions that, when executed, cause a computer to perform operations including receiving one or more n-gram vectors for a file as input to a file classifier, where the one or more n-gram vectors indicate occurrences of character pairs in printable characters representing the file. The operations also include generating, by the file classifier, output including classification data associated with the file based on the one or more n-gram vectors, where the classification data indicates whether the file includes malware.

In a particular aspect, a computing device includes a memory configured to store instructions to execute a file classifier and a processor configured to execute the instructions from the memory. Execution of the instructions causes the processor to perform operations including accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. The operations also include generating n-gram vectors for the multiple files by, for each file, generating an n-gram vector indicating occurrences of character pairs in printable characters representing the file. The operations further include generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data.

In another particular aspect, a method includes accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. The method also includes generating n-gram vectors for the multiple files by, for each file, generating an n-gram vector indicating occurrences of character pairs in printable characters representing the file. The method further includes generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data.

In another particular aspect, a computer-readable storage device stores instructions that, when executed, cause a computer to perform operations including accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. The operations also include generating n-gram vectors for the multiple files by, for each file, generating an n-gram vector indicating occurrences of character pairs in printable characters representing the file. The operations further include generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data.

In another particular aspect, a computing device includes a memory configured to store instructions to execute a file classifier and a processor configured to execute the instructions from the memory. Execution of the instructions causes the processor to perform operations including receiving one or more n-gram vectors for a file as input to a file classifier, where the one or more n-gram vectors indicate occurrences of groups of entropy indicators in a sequence of entropy indicators representing the file. The operations also include generating, based on the one or more n-gram vectors, output including classification data associated with the file, the classification data indicating whether the file includes malware.

In another particular aspect, a method includes receiving one or more n-gram vectors for a file as input to a file classifier, where the one or more n-gram vectors indicate occurrences of groups of entropy indicators in a sequence of entropy indicators representing the file. The method also includes generating, based on the one or more n-gram vectors, output including classification data associated with the file, the classification data indicating whether the file includes malware.

In another particular aspect, a computer-readable storage device stores instructions that, when executed, cause a computer to perform operations including receiving one or more n-gram vectors for a file as input to a file classifier, where the one or more n-gram vectors indicate occurrences of groups of entropy indicators in a sequence of entropy indicators representing the file. The operations also include generating, based on the one or more n-gram vectors, output including classification data associated with the file, the classification data indicating whether the file includes malware.

In another particular aspect, a computing device includes a memory configured to store instructions to execute a file classifier and a processor configured to execute the instructions from the memory. Execution of the instructions causes the processor to perform operations including accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. The operations also include generating a sequence of entropy indicators for each of the multiple files, each entropy indicator of the sequence of entropy indicators for the particular file corresponding to a chunk of the particular file. The operations further include generating n-gram vectors for the multiple files, where the n-gram vector for the particular file indicates occurrences of groups of entropy indicators in the sequence of entropy indicators for the particular file. The operations also include generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data.

In another particular aspect, a method includes accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. The method also includes generating a sequence of entropy indicators for each of the multiple files, each entropy indicator of the sequence of entropy indicators for the particular file corresponding to a chunk of the particular file. The method further includes generating n-gram vectors for the multiple files, where the n-gram vector for the particular file indicates occurrences of groups of entropy indicators in the sequence of entropy indicators for the particular file. The method also includes generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data.

In another particular aspect, a computer-readable storage device stores instructions that, when executed, cause a computer to perform operations including accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. The operations also include generating a sequence of entropy indicators for each of the multiple files, each entropy indicator of the sequence of entropy indicators for the particular file corresponding to a chunk of the particular file. The operations further include generating n-gram vectors for the multiple files, where the n-gram vector for the particular file indicates occurrences of groups of entropy indicators in the sequence of entropy indicators for the particular file. The operations also include generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a first example of a system that is operable to generate and use a trained file classifier for malware detection;

FIG. 2A illustrates a second example of a system that is operable to generate and use a trained file classifier for malware detection;

FIG. 2B illustrates an example of a server hierarchy that is operable to support malware detection and protection;

FIGS. 2C, 2D, 2E, and 2F illustrate examples of operation at the server hierarchy of FIG. 2B;

FIG. 3 illustrates a particular example of a process of generating a trained file classifier for malware detection;

FIG. 4 illustrates a first particular example of feature extraction and vector generation to generate or use a trained file classifier for malware detection;

FIG. 5 illustrates a second particular example of feature extraction and vector generation to generate or use a trained file classifier for malware detection;

FIG. 6 illustrates a first example of a process of using a trained file classifier for malware detection;

FIG. 7 illustrates a second example of a process of using a trained file classifier for malware detection;

FIG. 8 is a flowchart illustrating a particular example of a method of generating a trained file classifier;

FIG. 9 is a flowchart illustrating a particular example of a method of using a trained file classifier;

FIG. 10 is a flowchart illustrating another particular example of a method of generating a trained file classifier;

FIG. 11 is a flowchart illustrating another particular example of a method of using a trained file classifier;

FIG. 12 is a flowchart illustrating a particular example of a method of operation at the server hierarchy of FIG. 2B; and

FIG. 13 is a flowchart illustrating another particular example of a method of operation at the server hierarchy of FIG. 2B.

DETAILED DESCRIPTION

FIG. 1 illustrates a first example of a system 100 that is operable to generate and use a trained file classifier for malware detection. The system 100 includes a first computing device 110 configured to generate file classifiers and a second computing device 120 configured to use a file classifier to determine whether or not a particular file includes malware. Although illustrated as two distinct computing devices in FIG. 1, in some implementations, the first computing device 110 may include the second computing device 120. For example, the first computing device 110 may be configured to both generate and use the file classifier.

In FIG. 1, the first computing device 110 includes a processor 111 and a memory 112.

The memory 112 stores instructions executable by the processor 111 to generate a file classifier 116. For example, the memory 112 may store an automated model building application or an application that is executable to access training data, generate a data model based on training data 102, and output data corresponding to the data model as the file classifier. As used herein, a “file classifier” refers to a data model or a set of parameters that characterize a data model, where the data model is generated via a machine learning technique, such as by iterative modification of parameters based on supervised training data. As such, the file classifier disclosed herein may also be referred as a “trained file classifier” to denote that the file classifier is generated, based on supervised training data, using a machine learning technique.

The training data 102 may include a plurality of files 104 and classification data 103 associated with each of the files 104. The classification data 103 for a particular file indicates whether or not the particular file includes malware. Alternately, or in addition, if the particular file includes malware, the classification data 103 may indicate a particular type of malware (e.g., a trojan, a worm, spyware, adware, etc.) contained by the particular file.

In FIG. 1, the instructions executable by the processor 111 to generate the file classifier 116 based on the training data 102 include feature extraction instructions 113, vector generation instructions 114, and classifier generation instructions 115. As described further below, the feature extraction instructions 113 are executable to extract features from one or more of the files 104 and to provide the features to the vector generation instructions 114. Examples of features that may be extracted by the feature extraction instructions 113 include a string (or set of characters) representing binary content of a particular file of the files 104. Another example of features that may be extracted by the feature extraction instructions 113 includes entropy indicators representing binary content of a particular file of the files 104.

The vector generation instructions 114 are executable by the processor 111 to generate a feature vector based on features extracted by the feature extraction instructions 113. The feature vector, in combination with a corresponding portion of the classification data 103, is used as supervised training data to generate and test the file classifier. For example, supervised training data for a particular file may include one or more feature vectors (e.g., vectors of feature n-grams, as described further below) and classification information associated with the particular file.

The classifier generation instructions 115 uses a machine learning technique (e.g., an iterative model building process) to build a data model of a portion of the training data 102. The data model is iteratively trained, e.g., using a gradient decent process, to reduce or minimize an error between a classification output by the data model for a particular file and a classification assigned to the particular file in the classification data 103. After a training criterion is satisfied (e.g., after a particular number of iteration, or after additional iterations cease to reduce the error), a second portion of the training data 102 is used to test the data model. During testing of the data model, a file from the training data 102 that was not used to generate the data model is processed to generate a feature vector as input to the data model. A classification of the file assigned by the data model is compared to classification data associated with the file to determine whether the data model correctly classified the file. After multiple tests, if the data model performs adequately (e.g., the data model correctly classifies a threshold portion of tested files) data representing the data model may be output as a file classifier 116. If the data model does not perform adequately (e.g., the data model incorrectly classifies a threshold portion of tested files) additional training iterations may be performed.

After the first computing device 110 generates the file classifier 116, the file classifier 116 may be provided (e.g., via a network 140) to the second computing device 120 for use in detecting malware. For example, the second computing device 120 may receive a file 128 from another computing device (not shown) or may access the file 128 from a memory 122 of the second computing device 120. The second computing device 120 may process the file 128 using a classification application 124, as described further below, to determine whether the file 128 contains malware. The second computing device 120 may include a smart phone, a mobile communication device, a portable computer, a stationary (e.g., desktop or rack-mounted) computer, a tablet, a personal digital assistant (PDA), a set top box device, a video player, an entertainment unit, a display device, a television, a gaming console, a music player, a radio, a camera, a navigation device, an on-board component of a vehicle, an “internet-of-things” device (such as a smart home controller or sensor), or a combination thereof. Further, although only one second computing device 120 is illustrated in FIG. 1, in other implementations, the first computing device 110 may distribute the file classifier 116 to many other computing devices. For example, each of the other computing devices may include an instance of the classification application 124 and the file classifier 116. In this example, the file classifier 116 may be updated occasionally, and a new version of the file classifier 116 may be provided to the other computer device, e.g., as part of a malware detection update service.

The second computing device 120 may include the memory 122 and processor 121. The processor 121 may be configured to execute instructions from the memory 122 to perform various operations. For example, the memory 122 may include one or more applications 123, such as a browser application, a word processing application, an e-mail application, and so forth. The applications 123 may also include an operating system and associated utility applications, such as a file browser. In FIG. 1, the applications 123 also include the classification application 124.

The classification application 124 may be configured to utilize the file classifier 116 as a data model to scan the file 128 to determine whether the file 128 includes malware. For example, the classification application 124 includes the feature extraction instructions 125, which are executable to extract features from the file 128. In a particular implementation, the feature extraction instructions 125 perform operations similar to the operations performed by the feature extraction instructions 113. For example, if the feature extraction instructions 113 generate file features by extracting printable characters, then the feature extraction instructions 125 also generate file features by extracting printable characters. As another example, if the feature extraction instructions 113 generate file features by generating entropy indicators, then the feature extraction instructions 125 also generate file features by extracting entropy indicators. As yet another example, if the feature extraction instructions 113 generate file features by extracting printable characters and entropy indicators, then the feature extraction instructions 125 also generate file features by extracting printable characters and entropy indicators.

The classification application 124 also includes the vector generation instructions 126, which are executable to generate a feature vector or feature vectors based on the features of the file 128 provided by the feature extraction instructions 125. In a particular implementation, the vector generation instructions 126 perform operations similar to the operations performed by the vector generation instructions 114. For example, if the vector generation instructions 114 generate multiple n-gram feature vectors (as describe further below), then the vector generation instructions 126 also generate multiple n-gram feature vectors.

The feature vector or feature vectors generated by the vector generation instructions 126 are provided as input to the file classifier 116. The file classifier 116 generates a classification result indicating whether or not the file 128 includes malware. An example of use of the file classifier 116 by the classification application 124 is described with reference to FIG. 6.

The processor 121 may store information indicating the classification result at the memory 122. For example, a file identifier 130 of the file 128 and the classification result of the file 128 may be stored as file classification data 129. In some implementations, the file identifier 130 of the file 128 may include or correspond to a hash value based on the file 128. For example, the file identifier 130 may include a hash value of the feature vector or feature vectors of the file 128. In such implementations, the file identifier 130 and file classification data 129 may be retained in the memory 122 as a whitelist or blacklist. For example, when a new file, not shown, is received at the second computing device 120, the feature extraction instructions 125 may extract file features from the new file, and the vector generation instructions 126 may generate feature vectors of the new file. The classification application 124 may generate a hash value based on the feature vectors of the new file, and the hash value of the feature vectors of the new file may be compared to file identifiers 130 in the memory 122 to determine whether the new file has been classified by the file classifier 116. If the new file has already been classified by the file classifier 116, the processor 121 may forego classification of the new file using the file classifier 116. However, if the new file was classified using a prior file classifier (or a prior version of the file classifier 116), the processor 121 provide the feature vectors of the new file to the file classifier 116 to determine whether the file classifier 116 detects malware that was undetected by the prior file classifier or fails to detect malware that was detected by the prior file classifier.

In certain implementations, the classification application 124 may be configured to provide an output to a display device 131. For example, when the file 128 contains malware, the classification application 124 may provide an alert via the display device 131 to notify a user that the file 128 is unsafe.

FIG. 2A illustrates a second example of a system 200 that is operable to generate and use a trained file classifier for malware detection. The system 200 includes a first computing device 110 configured to generate a file classifier, a second computing device 120 configured to use the file classifier to determine whether or not a particular file includes malware, and a third computing device 150 configured to send file features of a file to be analyzed using the file classifier to the second computing device 120. Although the first computing device 110 and the second computing device 120 are illustrated as two distinct computing devices in FIG. 2A, in some implementations, the first computing device 110 may include the second computing device 120. For example, the first computing device 110 may be configured to both generate and use the file classifier. Further, although only one second computing device 120 is illustrated in FIG. 2A, in other implementations, the first computing device 110 may distribute the file classifier 116 to many other computing devices, such as server computing devices or client computing devices.

The first computing device 110 of FIG. 2A is includes the same features as and operates in the same manner as the first computing device 110 of FIG. 1. However, in FIG. 2A, functions associated with the classification application 124 of FIG. 1 are divided between the second computing device 120 and the third computing device 150. For example, the second computing device 120 includes a classification server application 170, and the third computing device 150 includes a classification client application 154. The third computing device 150 may include a smart phone, a mobile communication device, a portable computer, a stationary (e.g., desktop or rack-mounted) computer, a tablet, a personal digital assistant (PDA), a set top box device, a video player, an entertainment unit, a display device, a television, a gaming console, a music player, a radio, a camera, a navigation device, an on-board component of a vehicle, an “internet-of-things” device (such as a smart home controller or sensor), or a combination thereof. In a particular aspect, the third computing device 150 may be relatively resource constrained. For example, a processor 151 of the third computing device 150 may have less processing capability than the processor 121 of the second computing device 120. As another example, the third computing device 150 may be portable, and may therefore have a portable power supply, such as a battery. To avoid straining the resources of the third computing device 150, the third computing device 150 may offload some processing, such as file classification, to the second computing device 120, as described further below.

The third computing device 150 may include a memory 153 and the processor 151. The processor 151 may be configured to execute instructions from the memory 153 to perform variously operations. For example, the memory 153 may include one or more applications, such as a browser application, a word processing application, an e-mail application, and so forth. The memory 153 may also include an operating system and associated utility applications, such as a file browser. In FIG. 2A, the memory 153 also include the classification client application 154.

The classification client application 154 may be configured to send data characteristic of a file 128 to the second computing device 120 via a network 161. For example, the classification client application 154 may include the feature extraction instructions 125. In a particular implementation, the feature extraction instructions 125 perform operations similar to the operations performed by the feature extraction instructions 113. For example, if the feature extraction instructions 113 generate file features by extracting printable characters, then the feature extraction instructions 125 also generate file features by extracting printable characters. As another example, if the feature extraction instructions 113 generate file features by generating entropy indicators, then the feature extraction instructions 125 also generate file features by extracting entropy indicators. As yet another example, if the feature extraction instructions 113 generate file features by extracting printable characters and entropy indicators, then the feature extraction instructions 125 also generate file features by extracting printable characters and entropy indicators.

In a particular implementation, to determine whether the file 128 includes malware, the feature extraction instructions 125 generate file features 160 based on the file 128, and the classification client application 154 sends the file features 160 to the second computing device 120. For example, the file features 160 may include a string of printable characters extracted from the file 128. As another example, the file features 160 may include entropy indicators determined based on the file 128. In some implementations, the classification client application 154 may also include vector generation instructions (such as the vector generation instructions 126 of the second computing device 120). In such examples, the file features 160 may include or correspond to vectors determined based on the features extracted by the feature extraction instructions 125.

The second computing device 120 may provide the file features 160 to the classification server application 170. In the example illustrated in FIG. 2A, the classification server application 170 includes the vector generation instructions 126. The vector generation instructions 126 are executable to generate a feature vector or feature vectors based on the file features 160. In a particular implementation, the vector generation instructions 126 perform operations similar to the operations performed by the vector generation instructions 114. For example, if the vector generation instructions 114 generate multiple n-gram feature vectors (as describe further below), then the vector generation instructions 126 also generate multiple n-gram feature vectors. In implementations in which the third computing device 150 includes the vector generation instructions 126, the classification server application 170 may omit (e.g., not include) the vector generation instructions 126.

The classification server application 170 provides the feature vector or feature vectors to the file classifier 116. The file classifier 116 generates a classification result 162 indicating whether or not the file 128 includes malware. The second computing device 120 sends data indicating the classification result 162 to the third computing device 150 via the network 161.

The second computing device 120, the third computing device 150, or both, may store the classification result at a corresponding memory 122, 153. For example, the processor 121 may store a file identifier 130 of the file 128 and the classification result 160 as file classification data 129. As explained above, the file identifier 130 of the file 128 may include or correspond to a hash value based on the file 128, such as a hash value of the feature vector or feature vectors of the file 128. The file identifier 130 and file classification data 129 may be retained in the memory 122, in the memory 153, or both, as a whitelist or blacklist.

In certain implementations, the classification client application 154 may be configured to provide an output to a display device 152 of the third computing device 150. For example, when the file 128 contains malware, the classification client application 154 may provide an alert via the display device 152 to notify a user that the file 128 is unsafe.

FIG. 2B illustrates a particular example of a hierarchical system 250 of servers configured to support malware detection and protection. In an illustrative example, one or more components of the system 100 of FIG. 1 and/or the system 200 of FIG. 2A may be included in the system 250.

At a top level of the system 250 are one or more “master” servers 251. In some examples, the master servers 251 include cloud-based network accessible servers. The master servers 251 store or otherwise have access to a “base” file classification model 252 and a “base” prediction cache 253. The base file classification model 252 is trained using known malware and benign files, as described with reference to FIGS. 1, 2A, and 3. In some examples, as further described herein, the base file classification model 252 at the master server(s) 251 may not be used to determine file classifications. Rather, models present at individual enterprises may be used to determine file classifications. The base prediction cache 253 may maps files to benign/malicious classifications. When a file is classified for the first time, one or more attributes of the file (e.g., the feature vector extracted by the endpoint device that encountered the file, a secure hash algorithm (SHA)-256 cryptographic hash value of the file, etc.) may be inserted into the base prediction cache 253 along with the corresponding benign/malicious classification. When an endpoint device subsequently encounters the same file, the base prediction cache 253 may be used to quickly determine the classification for the file without having to evaluate the file using a file classification model.

At a second level of the system 250 are enterprise servers 255, 256, 257 that are specific to individual enterprises (e.g., business entities, governmental organizations, etc.). In the illustrated example, the three enterprises are called “Enterprise 1,” “Enterprise 2,” and “Enterprise 3.” It is to be understood, however, that in alternative examples there may be more or fewer enterprises, enterprise servers, and master servers in the system 250. The enterprise servers 255-257 may be configured to communicate with the master server(s) 251 via one or more public and/or private networks 254, which may include the Internet.

Each enterprise server 255-257 may be a part of a private network/subnet associated with the corresponding enterprise. In FIG. 2B, the base file classification model 252 and base prediction cache 253 associated with the master servers 251 are denoted as “base” versions because they are not tailored to specific enterprises. Some enterprise servers may store or have access to an (e.g., local) enterprise-specific file classification model and/or an (e.g., local) enterprise-specific prediction cache, while other enterprise servers may rely on the base file classification model 252 and the base prediction cache 253 at the master servers 251. In the illustrated example, enterprise-specific file classification models 258, 259, and 260 and enterprise-specific prediction caches 261, 262, and 263 are shown for each of the enterprise servers 255, 256, and 257, respectively.

An enterprise-specific file classification model for an enterprise may be generated from the base file classification model 252 by running training iterations and/or using training data associated with threats that are more likely to be encountered by the endpoint devices associated with that enterprise. For example, an enterprise-specific file classification model may be created for a chain of retail stores by subjecting the base file classification model 252 to additional training iterations based on known malware files and/or benign files that were used to train the base file classification model 252 but are predicted to be more frequently encountered by endpoint devices operated by retail store employees or located in retail spaces (e.g., point-of-sale devices). As another example, a file classification model that is specific to a particular government/intelligence/military agency may additionally be trained on classified threats and/or white-hat threats generated by the agency itself, where such threats are not used to train the base file classification model 252. Thus, in some scenarios, an enterprise-specific file classification model (e.g., one of the models 258-260) may produce a classification output that differs (e.g., at least in confidence level) as compared to another enterprise-specific file classification model (e.g., another of the models 258-260) and/or to the base file classification model 252.

In some examples, an enterprise-specific prediction cache (e.g., the caches 261-263) includes at least all of the records stored in the base prediction cache 253, and additionally includes classifications determined for zero-day files encountered by the endpoint devices of that enterprise.

Operation at the system 250 is how described with reference to FIGS. 2C-2F. Referring to FIG. 2C, an endpoint device 270 associated with Enterprise 1 may encounter (e.g., download) a file 280 that has not previously been encountered by any other device in the system 250. The endpoint device 270 may send first file attribute(s) 282, such as a SHA-256 value of the file 280, to its enterprise server 255.

The enterprise server 255 may check its local enterprise prediction cache 261 to determine if a file corresponding to the received first file attribute(s) 282, such as the received SHA-256 value, has previously been classified. In this example, a previously determined classification for the file 280 is not available, because the file 280 has not previously been encountered by any device in the system 250. Continuing to FIG. 2D, the enterprise server 255 may then forward the first file attribute(s) 282, to the master server(s) 251. Again, because the file 280 has not previously been encountered by any device in the system 250, a classification for the file 280 is not available in the base prediction cache 253 either. Thus, the master server(s) 251 may notify the enterprise server 255 that a classification for the file 280 is unavailable in the base prediction cache 253.

Continuing to FIG. 2E, because the file 280 has not previously been classified in the system 250, the enterprise server 255 may request the endpoint device 270 for second file attribute(s) 284 of the file 280. In an illustrative example, the second file attribute(s) 284 may be an extracted feature vector. For example, the second file attribute may include one or more n-gram vectors indicating occurrences of character pairs in printable characters representing the file 280. As another example, the second file attribute may include a sequence of entropy indicators, each entropy indicator of the sequence of entropy indicators corresponding to a chunk of the file 280. The enterprise server 255 may determine and store a classification 286 for the file 280 based on its local file classification model 258. The enterprise server 255 may provide the classification 286 to the endpoint device 270. Generation of extracted feature vectors is further described with reference to FIGS. 4-5. Use of a file classification model to determine a file classification is further described with reference to FIGS. 6-7.

At this point in time, the prediction cache 261 at Enterprise 1 includes an entry that is not available in the base prediction cache 253 or in any other enterprise-specific prediction cache at any other enterprise. If Enterprise 1 has opted in to a shared anti-malware ecosystem, then the enterprise server 255 may provide at least certain file attributes (e.g., a SHA-256 value, extracted feature vector(s), etc.) and the classification 286 upstream to the master server(s) 251 for addition to the base prediction cache 253 as a cache entry 290.

Advancing to FIG. 2F, if the same file 280 is subsequently encountered by an endpoint device at a different enterprise, then the classification 286 for the file 280 will be available in the entry 290 of the base prediction cache 253. Consequently, once the file 280 is classified as malicious by any server in the shared anti-malware ecosystem, all endpoint devices at all other enterprises may effectively be protected from the file 280, by virtue of their respective enterprise servers checking with the master server(s) 251 and the base prediction cache 253 when an unknown file is encountered. In some aspects, if the file 280 was determined to be malicious, the classification 286 may be used during training of a next version of the base file classification model 252 and/or enterprise-specific file classification models (e.g., the models 258, 259, and/or 260). In a particular implementation, files classified as benign are not used when training the subsequent models, so that if a file was misclassified the misclassification does not become learned behavior.

It will be appreciated that the hierarchical system 250 may provide real-time or near-real-time inoculation from newly detected malware threats across multiple enterprises. A file encountered by an endpoint device at one enterprise can result in near-immediate knowledge and protection for endpoint devices at that enterprise and at other enterprises. To illustrate, if an endpoint device at Enterprise 2 encounters the same file 280, the endpoint device may send a file attribute 287 (e.g., SHA-256 value) of the file to the enterprise server 256, which may forward the file attribute 287 to the master server(s) 251. A cache hit occurs (e.g., the file attributes 282 and 287 are the same SHA-256 value) at the base prediction cache 253 because the file 280 was previously classified at Enterprise 1. In some examples, the classification 286 for the file provided by the master server(s) 251 may be stored at the local prediction cache 262 for Enterprise 2, so that subsequent classification requests for the file 280 within Enterprise 2 do not require communicating upstream with the master server(s) 251.

It will be appreciated that because of the large number of zero-day threats that appear daily, and because of the unpredictable speed with which threats spread across geographic regions, the near-immediate worldwide inoculation provided by the system 250 may be a valuable tool to combat malware.

In certain aspects, one or more enterprises may opt out of the shared anti-malware ecosystem. For example, in FIG. 2B, Enterprise 3 has opted out of the shared anti-malware ecosystem, as shown by the unidirectional arrow to Enterprise 3. Consequently, classifications added to the base prediction cache 253 may be provided downstream to Enterprise 3, but classifications generated at Enterprise 3 may not be shared upstream to the master servers 251 or with other enterprises. To illustrate, Enterprise 3 may be a governmental/intelligence/military organization that is siloed for secrecy reasons. In some completely siloed examples, an Enterprise may be cut off—for example rather than requesting the master server(s) 251 to check the base prediction cache 253, the Enterprise may periodically or occasionally receive a copy of the base prediction cache 253 and may use its own copy. Thus, other than periodic or occasional cache/model updates, the Enterprise may not communicate with the rest of the system 250. However, in some examples a siloed enterprise may receive base prediction cache updates, so the siloed enterprise may nonetheless benefit from the near immediate zero-day protection described herein. Alternatively, base prediction cache updates may not be sent to siloed enterprises, for example to address a scenario where a file classified as malicious was actually generated by a government/intelligence/military agency, and therefore the agency does not need “protection” from the file.

FIG. 3 illustrates a particular example of a process 300 of generating a trained file classifier for malware detection. In FIG. 3, the files 104 of the training data 102 include a first file 202, a second file 203, and one or more additional files including an Mth file 204. The classification data 103 includes a classification associated with each of the files 104. For example, a first classification 212 is associated with the first file 202, a second classification 213 is associated with the second file 203, and an Mth classification 214 is associated with the Mth file 204.

To generate the file classifier 116, one or more of the files 104 is provided to the feature extraction instructions 113. For example, as illustrated in FIG. 3, a file 220 is provided to the feature extraction instructions 113 to generate file features 221. The file features 221 may include a set of characters (e.g., a string) representing binary values of the file 220. For example, as describe with reference to FIG. 4, the file 220 may be a binary file, and the feature extraction instructions 113 may convert the binary file into a set of alphanumeric characters, American Standard Code for Information Interchange (ASCII) characters, or Unicode characters. To illustrate, the feature extraction instructions 113 may performing a binary-to-ASCII conversion, using a utility such as the UNIX strings utility. In some examples, only particular characters or sets of characters are used to form the file features 221. To illustrate, the file features 221 may include only printable characters generated by the binary-to-ASCII conversion or only a subset of the printable characters generated by the binary-to-ASCII conversion (such as only lowercase letters and spaces, as illustrated in FIG. 4). Additionally, or in the alternative, the file features 221 may omit groups of characters that are shorter than a threshold length, such as set of characters including fewer than four characters.

Instead of or in addition to converting the file 220 into a set of alphanumeric characters, the feature extraction instructions 113 may generate the file features 221 based on entropy information associated with the particular file 220, as describe further with reference to FIG. 5. For example, the file 220 may include a binary file, and the feature extraction instructions 113 may generate an entropy value for each block of a particular size of the particular file 220. The entropy values may be used to generate entropy indicators represented by, for example, alphanumeric characters. In this example, the file features 221 provided to the vector generation instructions 114 include the entropy indicators.

In some examples, the feature extraction instructions 113 perform both string extraction and entropy indicator extraction. In such examples, the file features 221 include the entropy indicators and sets of characters (e.g., printable ASCII characters).

The vector generation instructions 114 are configured to generate a vector representing n-grams of the file features 221. In this context, an n-gram refers to sequence of n values in the file features 221, where n is a positive integer greater than or equal to two. In some implementations, as describe further below, the vector generation instructions 114 may generate more than one vector based on the file features 221 of the file 220. In such implementations, the n-grams used to generate the vectors may include contiguous sequences of values (i.e., zero-skip grams), discontinuous sequences of values (i.e., skip grams), or both.

For example, as described in the example of FIG. 4, the file features 221 may include printable characters representing the binary values of the file 220, and the file vector 222 may include values representing occurrence of n-grams (e.g., pairs when n=2, triplets when n=3, etc.) of printable characters of the file features 221. The n-grams may represent adjacent printable characters within the file features 221. Additionally, or in the alternative, the file vector 222 may represent nonadjacent characters from the file features 221. To illustrate, for a bi-gram (e.g., n=2), a pair of nonadjacent characters of the file features 221 may include characters that are separated by at least one other character (e.g., a one-skip gram), at least two other characters (e.g., a two-skip gram), at least three other characters (e.g., a three-skip gram), at least four other characters (e.g., a four-skip gram), etc. In another example, the file vectors 222 may include n-grams of more than two characters, such as a three character n-gram (e.g., n=3).

In a particular implementation, the file vector 222 includes values representing occurrence of n-grams (e.g., pairs of characters, groups of characters, pairs of entropy indicators, or groups of entropy indicators) in the file features 221. For example, as illustrated in greater detail in FIG. 4, a particular file vector may indicate occurrence of zero-skip, bi-grams in the file features 221. In this example, the particular file vector includes one field for each possible bi-gram (based on characters that are permitted to be included in the file features 221). To illustrate, if the feature extraction instructions 113 generate the file features 221 such that the file features 221 include only lowercase English letters and spaces, then there are 27 distinct characters permitted in the file features 221 (corresponding to a-z and a space character). Each bi-gram may include any of the 27 permitted characters as a first character and any of the 27 permitted characters as a second character. Thus, there are 27×27 (or 729) possible bi-grams based on the characters permitted in the file features 221. In this example, each file vector may include 729 fields, each field indicate occurrence of a corresponding bi-gram.

In a particular implementation, the file vector 222 is a Boolean vector (i.e., a vector of Boolean values). In this particular implementation, a value in each field is either a one (1) indicating that the n-gram appears in the file features 221 at least once or is a zero (0) indicating that the n-gram does not appear in the file features 221. In another particular implementation, each field of the file vector 222 represents a count of occurrences of the corresponding n-gram. In this particular implementation, the value in a particular field is a zero (0) indicating that the n-gram does not appear in the file features 221, or is an integer indicating the number of times that the n-gram appears in the file features 221.

In some implementations, the vector generation instructions 114 generate more than one file vector for each file. For example, the vector generation instructions 114 may generate a file vector based on adjacent characters (e.g., zero-skip n-grams) of the file features 221 and may generate one or more other file vectors based on non-adjacent characters (e.g., skip n-grams) of the file features 221. To illustrate, the vector generation instructions 114 may generate a first file vector based on zero-skip bi-grams (e.g., adjacent pairs of characters) of the file features 221, may generate a second file vector based on one-skip bi-grams (e.g., pairs of characters with one intervening character) of the file features 221, may generate a third file vector based on two-skip bi-grams (e.g., pairs of characters with two intervening character) of the file features 221, may generate a fourth file vector based on three-skip bi-grams (e.g., pairs of characters with three intervening character) of the file features 221, and may generate a fifth file vector based on four-skip bi-grams (e.g., pairs of characters with four intervening character) of the file features 221. In this example, the first, second, third, fourth, and fifth file vectors may be concatenated to form the file vector 222 provided with the file classification 223 of the file 220 as part of the supervised training data 224. In other examples, more than or fewer than five file vectors based on different skip grams may be used. Additionally, or in the alternative, one or more file vectors based on n-grams of entropy indicators may be combined with (e.g., concatenated with) one or more file vectors based on n-grams of printable characters as part of the supervised training data 224.

The classifier generation instructions 115 use the supervised training data 224 to generate the file classifier 116. For example, the classifier generation instructions 115 may utilize an automated neural network building process, a support vector machine building process, a gradient descent boosting decision tree building process, or another process to build the file classifier 116 using a machine learning technique. The file classifier 116 (or data describing the file classifier 116) may be stored at the memory 112. Additionally, the file classifier 116 may be provided to other computing devices, such as the second computing device 120 of FIG. 1, the second computing device 120 of FIG. 2, or both.

FIG. 4 illustrates a first particular example of operations performed by the feature extraction instructions 113 and the vector generation instructions 114. In particular, FIG. 4 illustrates generating file features by converting a binary file 310 into a set of characters, and generating multiple file vectors based on n-grams of the file features. As explained with reference to FIGS. 1 and 2A, the feature extraction instructions 125 and the vector generation instructions 126 may perform the same operations as the feature extraction instructions 113 and the vector generation instructions 114 on a file to be classified.

In FIG. 4, the feature extraction instructions 113 receive the binary file 310. The binary file 310 may include an executable file, such as one of the files 104 of FIG. 1-3. The feature extraction instructions 113 include string extraction instructions 311. The string extraction instructions 311 are configured to determine a set of characters 312 based on the binary file 310. For example, the string extraction instructions 311 may include or correspond to a UNIX strings utility, and the set of characters 312 may include ASCII characters represented by portions of the binary file. In a particular implementation, only a subset of ASCII characters are allowed in the set of characters 312. For example, the string extraction instructions 311 may generate only lowercase English letters, only uppercase English letters, only letters and numbers, or only printable ASCII characters. In the particular example illustrated in FIG. 4, the set of characters 312 includes only lowercase English letters and spaces represented by the binary file 310. Thus, in the example illustrated in FIG. 4, the set of characters 312 is a string of printable characters.

The feature extraction instructions 113 may provide the set of characters 312 to the vector generation instructions 114. In the particular example illustrated in FIG. 4, the vector generation instructions 114 illustrate generation of a first vector based on zero-skip bi-grams, generation of a second vector based on one-skip bi-grams, and generation of a third vector based on four-skip bi-grams. In other implementations, similar processes may be used to generate vectors based on bi-grams with different skip values (e.g., a two-skip bi-gram), vectors based on n-grams with a different value of n (e.g., tri-grams, where n=3), or a combination thereof (e.g., a vector based on a zero-skip tri-gram, a vector based on a one-skip tri-gram, etc.).

A first operation 320 illustrate forming a first vector (e.g., zero-skip vector 321) based on zero-skip bi-grams of the set of characters 312. Because the zero-skip vector 321 is based on zero-skip grams, each value of the zero-skip vector 321 represents a contiguous or adjacent group of characters. Further, since the zero-skip vector 321 is based on bi-grams, each of the contiguous or adjacent group of characters includes two characters. Thus, each value of the zero-skip vector 321 represents occurrence of a particular pair of adjacent characters in the set of characters 312. In particular, in FIG. 4, each zero value of the zero-skip vector 321 indicates that a corresponding pair of adjacent characters does not occur in the set of characters 312. To illustrate, a value of zero in a first field (corresponding to the bi-gram “aa”) of the zero-skip vector 321 indicates that no character “a” is adjacent to another character “a” in the set of characters 312. Also in FIG. 4, each non-zero value of the zero-skip vector 321 indicates that a corresponding pair of adjacent characters does occur in the set of characters 312. To illustrate, a value of one in a second field (corresponding to the bi-gram “ab”) of the zero-skip vector 321 indicates that at least one character “a” is adjacent to (e.g., immediately precedes) a character “b” in the set of characters 312. In the particular implementation illustrated in FIG. 4, each field of the zero-skip vector 321 represents a count of occurrences of the corresponding bi-gram. Thus, in this implementation, the value of one in the second field (corresponding to the bi-gram “ab”) of the zero-skip vector 321, indicates that the bi-gram “ab” occurs once in the set of characters. Similarly, in this implementation, the value of two in the field corresponding to the bi-gram “ek” indicates that the bi-gram “ek” occurs twice in the set of characters. In other implementations, the vector generation instructions 114 generate Boolean vectors. In such implementations, the value of each field is either a zero indicating that the corresponding bi-gram does not occur in the set of characters 312, or is a one indicating that the corresponding bi-gram occur at least once in the set of characters 312.

The zero-skip vector 321 has a length (e.g., a number of fields) corresponding to a number of possible bi-grams that could occur in the set of characters 312. Stated another way, the length of the zero-skip vector 321 is equal to P×P, where P is a count of distinct characters that the string extraction instructions 311 are allowed to generate. To illustrate, as explained above, in the example illustrated in FIG. 4, the string extraction instructions 311 are configured to extract only lowercase English letters and spaces. Accordingly, the count of distinct characters that the string extraction instructions 311 are allowed to generate equals 27, so P×P equals 27×27 or 729. Thus, in this example, the zero-skip vector 321 has a length of 729 fields, with each field corresponding to a particular bi-gram, and the entire set of 729 fields representing every possible bi-gram based on the distinct characters generated by the string extraction instructions 311. If the string extraction instructions 311 are allowed to extract more distinct characters, then the zero-skip vector 321 will be longer. For example, if the string extraction instructions 311 are allowed to extract as lower case English letters (26 distinct characters), numbers (10 distinct characters), and spaces (1 distinct character), then P=26+10+1, or 37 and P×P=37×37, or 1369 fields.

A second operation 330 of FIG. 4 illustrates forming a second vector (e.g., one-skip vector 331) based on one-skip bi-grams of the set of characters 312. Because the one-skip vector 331 is based on one-skip grams, each value of the one-skip vector 331 represents a non-adjacent group of characters (e.g., two characters with one character between them). Further, since the one-skip vector 331 is based on bi-grams, each non-adjacent group of characters includes two characters. Thus, each value of the one-skip vector 331 represents occurrence of a particular pair of characters separated in the set of characters 312 by one other character. In FIG. 4, each zero value of the one-skip vector 331 indicates that a corresponding pair of characters separated by one other character does not occur in the set of characters 312. To illustrate, a value of zero in a second field (corresponding to the bi-gram “ab”) of the one-skip vector 331 indicates that within the set of characters 312, no character “a” is separated from a character “b” by one intervening character. Also in FIG. 4, each non-zero value of the zero-skip vector 321 indicates that a corresponding pair of characters does occur in the set of characters 312 with one intervening character. To illustrate, a value of one in a first field (corresponding to the bi-gram “aa”) of the one-skip vector 331 indicates that at least one character “a” precedes a character “a” in the set of characters 312 with one character between them. In the particular implementation illustrated in FIG. 4, each field of the one-skip vector 331 represents a count of occurrences of the corresponding bi-gram. Thus, in this implementation, the value of one in the first field (corresponding to the bi-gram “aa”) of the one-skip vector 331, indicates that the one-skip bi-gram “aa” occurs once in the set of characters 312. Similarly, in this implementation, the value of two in the field corresponding to the one-skip bi-gram “ej” indicates that the one-skip bi-gram “ej” occurs twice in the set of characters 312. As explained above, in other implementations, the vector generation instructions 114 generate Boolean vectors rather than vectors based on counts of each bi-gram.

Since the one-skip vector 331 and the zero-skip vector 321 are both based on bi-grams of the set of characters 312, the one-skip vector 331 and the zero-skip vector 321 have the same length. Thus, the length of the one-skip vector 331 is P×P, as explained above with reference to the zero-skip vector 321.

A third operation 340 of FIG. 4 illustrates forming a third vector (e.g., four-skip vector 341) based on four-skip bi-grams of the set of characters 312. Because the four-skip vector 341 is based on four-skip grams, each value of the four-skip vector 341 represents a non-adjacent group of characters (e.g., two characters with four characters between them). Further, since the four-skip vector 341 is based on bi-grams, each non-adjacent group of characters includes two characters. Thus, each value of the four-skip vector 341 represents occurrence of a particular pair of characters separated in the set of characters 312 by four other character. Since the four-skip vector 341 is based on bi-grams, the four-skip vector 341 has the same length as the zero-skip vector 321 and the one-skip vector 331.

The vector generation instructions 114 may generate a single n-gram vector, such as the zero-skip vector 321 or may generate a set of n-gram vectors, such as the zero-skip vector 321, the one-skip vector 331, a two-skip vector (not shown), a three-skip vector (not shown), and the four-skip vector 341. Using multiple different skip grams (e.g., a zero-skip gram, a one-skip gram, a two-skip gram, a three-skip gram, and a four-skip gram) enable the vector generation instructions 114 to generate vectors that retain information about context of particular characters while constraining an overall size of the input data. For example, the length of each vector is fixed and known, as explained above. Thus, information about context of character pairs, such as presence of particular words or other patterns is retained without the use of a dictionary or other pattern information related to potential patterns within strings.

FIG. 5 illustrates a second particular example of operations performed by the feature extraction instructions 113 and the vector generation instructions 114. In particular, FIG. 5 illustrates generating entropy indicators as file features and generating one or more file vector based on n-grams of the entropy indicators. As explained with reference to FIGS. 1 and 2A, the feature extraction instructions 125 and the vector generation instructions 126 may perform the same operations as the feature extraction instructions 113 and the vector generation instructions 114 on a file to be classified.

In FIG. 5, the feature extraction instructions 113 receive the binary file 310. The binary file 310 may include an executable file, such as one of the files 104 of FIGS. 1-3. The binary file 310 is divided into chunks via chunking instructions 401. For example, the binary file 310 may be divided into chunks of 256 bytes each. In other examples, different chunk sizes may be used. When the binary file 312 has a length that is not divisible by 256 bytes without a remainder, the remainder is maybe dropped. While the chunks 402 are illustrated in FIG. 5 in binary form, in other implementations, the chunking instructions 401 may convert the binary file 310 into hexadecimal values, and the chunks 402 may include hexadecimal values.

In a particular implementation, the feature extraction instructions 113 include entropy calculation instructions 403. The entropy calculation instructions 403 may be configured to calculate an entropy (e.g., a Shannon entropy) for each of the chunks 402. For example, in FIG. 5, the binary file 310 is used to generate five chunks 402 and the entropy calculation instructions 403 generate data including five of entropy values 404. Entropy values may be calculated using Equation 1: H=−Σ _(i) P _(i) log₂ P _(i)  (Equation 1)

where H is entropy, and P, is probability of each combination of values i. In a particular implementation, each byte of each of the chunks 402 is represented by a pair of hexadecimal characters. There are 256 possible values for a pair of hexadecimal characters. Thus, in this implementation, the entropy values (H) range between zero and eight where the maximum entropy (eight) is reached when P, takes a constant value of 1/256 (i.e., every byte is completely random). In other implementations, other ranges of entropy values may be used depending on the chunking, how data within each chunk is grouped (e.g., into two hexadecimal values in the example above), and the base of the logarithm that is used to calculate the entropy.

The entropy values 404 may be used to generate entropy indicators. For example, the entropy values 404 may be processed by the entropy indicator generation instructions 405 to generate the entropy indicators 410. In a particular implementation, the entropy indicator generation instructions 405 perform a binning operation to assign each of the entropy values 404 to an entropy range bin corresponding to an entropy indicator. For example, in FIG. 5, the range of possible entropy values (0-8) is divided into 8 bins, and each bin is associated with a bin name. The bin names are used as entropy indicators.

To illustrate, in the example illustrated in FIG. 5, the entropy values 404 include a first entropy value of 1.30. The first entropy value of 1.30 is within a range of entropy values 1-2 associated with the second bin, and the bin name of second bin is “b”. Accordingly, an entropy indicator “b” is included as a first entry of entropy indicators 410. Similarly, the entropy values 404 include a second entropy value of 1.30, which is represented in a second entry of the entropy indicators 410 by an entropy indicatory “b”. Likewise, the entropy values 404 include a third entropy value of 7.35, which is represented in a third entry of the entropy indicators 410 by an entropy indicatory “h”.

The length (e.g., number of fields) of the entropy indicators 410 depends on the length of the file 310 (or how many chucks are generated based on the file 310). Accordingly, the entropy indicators 410 may have more or fewer values depending on the length of the binary file 310. Thus, although the entropy indicators 410 of FIG. 5 include six fields corresponding to entropy indicators “b,” “b,” “h,” “d,” “b,” and “a” this is for ease of illustration, and is not a limitation. Rather, different files are likely to have different file lengths, and thus, to generate different numbers of entropy indicators 410.

The vector generation instructions 114 may use a process similar to the process described in FIG. 4 in order to generate a file vector (or file vectors) having a known length based on the entropy indicators 410. For example, FIG. 5 illustrates the vector generation instructions 114 performing an operation 420 to generate a zero-skip vector 421 (e.g., a vector indicating occurrences of zero-skip bi-grams in the entropy indicators 410). The zero-skip vector 421 is generated in FIG. 5 using the same process that was used to generate the zero-skip vector 321 in FIG. 4. Thus, the zero-skip vector 421 has a length corresponding to the number of bins used by the entropy indicator generation instructions 405 squared. In the particular example illustrated in FIG. 5, the entropy indicator generation instructions 405 use eight bin; therefore, the zero-skip vector 421 has a length of 64 fields or values. As explained with reference to FIG. 4, the zero-skip vector 421 may be a Boolean vector, or each field of the zero-skip vector 421 may represent a count. If the zero-skip vector 421 is a Boolean vector, each field of the zero-skip vector 421 a binary value indicating whether the particular combination of entropy indicators is present in the entropy indicators 410. Alternatively, each field of the zero-skip vector 421 may have a value representing a count of occurrences of groups of entropy indicators in the entropy indicator 410.

In FIG. 5, the zero-skip vector 421 is illustrated as a bi-gram; however, in other implementations, the zero-skip vector 421 may include a tri-gram, a four-gram or an n-gram with a different value of n. For example, if the zero-skip vector 421 includes a tri-gram, each field of the zero-skip vector 421 represents occurrence of a series of three entropy indicators in the entropy indicators 410. In this example, the zero-skip vector 421 would have a length corresponding to P×P×P, where P=8, or 512 fields.

Additionally, although FIG. 5 illustrates only the zero-skip vector 421, in other implementations, the vector generation instructions 114 may use the entropy indicators 410 to generate a one-skip vector, a two-skip vector, a three-skip vector, or a four-skip vector, as described with reference to FIG. 4. Further, although FIG. 5 only illustrates the vector generation instructions 114 generating a single vector (i.e., the zero-skip vector 421), in other implementations, the vector generation instructions 114 in FIG. 5 may generate a set of vectors, such as the zero-skip vector 421 (i.e., a zero-skip bi-gram vector), a one-skip bi-gram vector (such as the one-skip vector 331 of FIG. 3), a two-skip bi-gram vector, a three-skip bi-gram vector, and a four-skip bi-gram vector (such as the four-skip vector 341 of FIG. 4).

The vector or vectors generated in FIG. 5 represent entropy values of regions (e.g., blocks) of the file 310 and patterns of entropy values within the file 310. One advantage of representing entropy values via the vector is that patterns of entropy values may be retained from one generation or version of a malware code to a subsequent generation of version of the malware code. For example, one method of obscuring or hiding malware code from a signature-based malware detection program is to encrypt the malware code within the binary file. After the encrypted portion of the malware code has been identified using a signature-based recognition system, the malware code can be re-encrypted using a different encryption key. Re-encrypting using a different encryption key may change the malware code sufficiently to elude detection of the malware code by signature-based recognition system. However, re-encrypting the malware code using a different key does not change the distribution of entropy values within the file. Accordingly, using information about the pattern or distribution of entropy within the file may be able to detect the re-encrypted malware code that would not be detected by a signature-based malware detection application.

FIG. 6 illustrates a first example of a process 600 of using the file classifier 116 for malware detection. In the example illustrated in FIG. 6, the file 128 is received at the second computing device 120 of FIG. 1, and the second computing device 120 executes the classification application 124 (based on the file classifier 116) to determine whether the file 128 includes malware.

In FIG. 6, the file 128 is provided to feature extraction instructions 125. The feature extraction instructions 125 are configured to extract file features 602 from the file 128. In a particular implementation, the feature extraction instructions 125 operate in the same manner as the feature extraction instructions 113 of FIGS. 1-5. For example, the file features 602 extracted from the file 128 by the feature extraction instructions 125 may include a set of characters (like the set of characters 312 of FIG. 4), may include entropy indicators (like the entropy indicators 410 of FIG. 5), or both.

The file features 602 may be provided to vector generation instructions 126. The vector generation instructions 126 operate in the same manner as the vector generation instructions 114 of FIGS. 1-5. For example, the vector generation instructions 126 may generate one or more feature vectors 604 based on the file features 602. The one or more feature vectors 604 may indicate occurrences of skip n-grams or zero-skip n-grams within the file features 602, as described with reference to FIGS. 1-5. For example, the one or more feature vectors 604 may include a vector representing presence of pairs of adjacent characters in a set of printable characters of the file features 602, may represent groups of entropy indicators in a sequence of entropy indicators of the file features 602, or both.

The one or more feature vectors 604 may be provided as input to the file classifier 116. As explained above, the file classifier 116 may include a deep neural network, a support vector machine, a decision tree, or another data structure generated to model the supervised training data 224 of FIG. 3. The file classifier 116 may generate data representing a classification result 606 for the file 128 based on the feature vector 604. For example, the classification result 606 may indicate whether or not the file 128 includes malware. Alternatively, the classification result 606 may indicate that the file 128 does not include malware, or that the file 128 includes a particular type of malware, such as a trojan, adware, or a worm. The classification result 606 may be stored in the memory 122 along with a file identifier (such as the file identifier 130 of FIG. 1) of the file 128. Alternatively, or in addition, the classification result 606 may be provided as an output to a user (e.g., via the display device 131 of FIG. 1) to indicate whether the file 128 is safe to use.

In a particular implementation, the classification result 606 and the file identifier 130 may be used as a white list or black list to check files received in the future. For example, when another file is received, a file identifier of the other file may be generated and checked against file identifiers in the memory 122 to determine whether the new file has been previously checked using the file classifier 116. In this example, one or more of the feature extraction instructions 125, the vector generation instructions 126, or the file classifier 116 may be executed based on determining that the file identifier of the new file does not match the identifier of a previously checked file.

FIG. 7 illustrates a second example of a process 700 of using the file classifier 116 for malware detection. In the example illustrated in FIG. 7, the file 128 is received at a client computing device (e.g., the third computing device 150 of FIG. 2), which sends file features to a server computing device (e.g., the second computing device 120), and the server computing device executes the classification application 124 (based on the file classifier 116) to determine whether the file 128 includes malware.

In FIG. 7, the file 128 is provided to the feature extraction instructions 125 of the client computing device. The feature extraction instructions 125 are configured to extract file features 602 from the file 128. In a particular implementation, the feature extraction instructions 125 operate in the same manner as the feature extraction instructions 113 of FIGS. 1-5. For example, the file features 602 extracted from the file 128 by the feature extraction instructions 125 may include a set of characters (like the set of characters 312 of FIG. 4), may include entropy indicators (like the entropy indicators 410 of FIG. 5), or both.

The client computing device sends the file features 602 to the server computing device.

The server computing device generates a feature vector 604 or feature vectors based on the file features 602. In other implementations, the client computing device includes the vector generation instructions 126, and the file features 602 include or correspond to the feature vector 604. The vector generation instructions 126 operate in the same manner as the vector generation instructions 114 of FIGS. 1-5. For example, the feature vectors 604 may indicate occurrences of skip n-grams or zero-skip n-grams within the file features 602, as described with reference to FIGS. 1-5. As another example, the feature vectors 604 may include a vector representing presence of pairs of adjacent characters in a set of printable characters of the file features 602, may represent groups of entropy indicators in a sequence of entropy indicators of the file features 602, or both.

The one or more feature vectors 604 may be provided as input to the file classifier 116.

As explained above, the file classifier 116 may include a deep neural network, a support vector machine, a decision tree, or another data structure generated to model the supervised training data 224 of FIG. 3. The file classifier 116 may generate data representing a classification result 606 for the file 128 based on the one or more feature vectors 604. For example, the classification result 606 may indicate whether or not the file 128 includes malware. Alternatively, the classification result 606 may indicate that the file 128 does not include malware, or that the file 128 includes a particular type of malware, such as a trojan, adware, or a worm. Data indicating the classification result 606 is sent to the client computing device. The classification result 606 may also be stored in the memory 122 along with a file identifier (such as the file identifier 130 of FIG. 1) of the file 128.

The classification client application 154 of the client computing device may generate an output (e.g., via the display device 131 of FIG. 1) to indicate whether the file 128 is safe to use. In some implementations, the classification client application 154 may quarantine or otherwise restrict access to or execution of the file 128 if the file 128 is determined to include malware.

FIG. 8 is a flowchart illustrating a particular example of a method 800 of generating a file classifier. The method 800 may be performed by the first computing device 110 of FIGS. 1 and 2A. For example, the processor 111 executing instructions from the memory 112 may perform the method 800.

The method 800 includes, at 801, accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicates, for a particular file of the multiple files, whether the particular file includes malware. For example, the first computing device 110 of FIG. 1 may access the training data 102, which includes the files 104 and the classification data 103 associated with the files 104.

The method 800 includes, at 802, processing each file to generate printable characters representing the file. For example, the processor 111 executing the feature extraction instructions 113 may generate the printable characters. To illustrate, the feature extraction instructions 113 may include the string extraction instructions 311 that are configured to process binary files to generate ASCII characters.

The method 800 includes, at 803, generating n-gram vectors for the multiple files by, for each file, generating an n-gram vector indicating occurrences of character pairs in printable characters representing the file. For example, the processor 111 executing the vector generation instructions 114 may generate the n-gram vectors for the multiple files. The n-gram vectors may include skip vectors, no-skip vectors, or both. To illustrate, the n-gram vectors may include one or more of the zero-skip vector 321 of FIG. 4, the one-skip vector 331 of FIG. 4, a two-skip vector, a three-skip vector, the four-skip vector 341 of FIG. 4, or a vector that uses an n-gram with different skip value. Further, the n-gram vectors may include one or more of a bi-gram, a tri-gram, or a vector that uses a different value of n.

The method 800 includes, at 804, generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data. For example, the processor 111 executing the classifier generation instructions 115 may generate the file classifier 116 using the n-gram vectors and the classification data 103 as supervised training data 224. The file classifier 116 may be stored in the memory 112, may be provided to the second computing device 120, or both.

FIG. 9 is a flowchart illustrating a particular example of a method 900 of using a file classifier. The method 900 may be performed by the second computing device 120 of FIG. 1. For example, the processor 121 executing the classification application 124 from the memory 122 may perform the method 900. In another implementation, the method 900 may be performed by the classification client application 154 in co-operation with the classification server application 170 of FIG. 2.

The method 900 includes, at 901, processing a file to generate the printable characters representing the file. For example, the processor 121 executing classification application 124 may generate the printable characters. To illustrate, the classification application 124 may include feature extraction instructions (such as the feature extraction instructions 113 or the string extraction instructions 311) that are configured to process binary files to generate the printable characters.

The method 900 includes, at 902, receiving one or more n-gram vectors for the file as input to a file classifier, where the one or more n-gram vectors indicates occurrences of character pairs in the printable characters representing the file. For example, the processor 121 executing the classification application 124 may receive one or more n-gram vectors for the file as input to the file classifier 116. In this example, the classification application 124 may include vector generation instructions (such as the vector generation instructions 114) that are configured to generate the n-gram vectors for the multiple files and to provide the n-gram vectors as input to the file classifier 116.

The method 900 includes, at 903, generating, by the file classifier, output including classification data associated with the file based on the one or more n-gram vectors, where the classification data indicate whether the file includes malware. For example, the processor 121 executing the classification application 124 may generate output including a classification result associated with the file 128.

FIG. 10 is a flowchart illustrating another particular example of a method 1000 of generating a file classifier. The method 1000 may be performed by the first computing device 110 of FIG. 1. For example, the processor 111 executing instructions from the memory 112 may perform the method 1000.

The method 1000 includes, at 1001, accessing information identifying multiple files and identifying classification data for the multiple files, where the classification data indicate, for a particular file of the multiple files, whether the particular file includes malware. For example, the first computing device 110 of FIG. 1 may access the training data 102, which includes the files 104 and the classification data 103 associated with the files 104.

The method 1000 includes, at 1002, generating a sequence of entropy indicators for each of the multiple files, each entropy indicator of the sequence of entropy indicators for the particular file corresponding to a chunk of the particular file. For example, the processor 111 executing the feature extraction instructions 113 may generate the sequence of entropy indicators. To illustrate, the feature extraction instructions 113 may include the chunking instructions 401 that parse each file to generate chucks (such as the chunks 402) based on each file. The feature extraction instructions 113 may also include the entropy calculation instructions 403 that are configured to determine an entropy value for each chuck. The feature extraction instructions 113 may further include the entropy indicator generation instructions 405 that are configured to determine an entropy indicator for each entropy value to generate the entropy indicators 410.

The method 1000 includes, at 1003, generating n-gram vectors for the multiple files, where the n-gram vector for the particular file indicates occurrences of groups of entropy indicators in the sequence of entropy indicators for the particular file. For example, the processor 111 executing the vector generation instructions 114 may generate the n-gram vectors for the multiple files. The n-gram vectors may include skip vectors, no-skip vectors, or both. To illustrate, the n-gram vectors may include one or more of the zero-skip vector 421 of FIG. 5, a one-skip vector, a two-skip vector, a three-skip vector, a four-skip vector, or a vector that uses an n-gram with different skip value. Further, the n-gram vectors may include one or more of a bi-gram, a tri-gram, or a vector that uses a different value of n.

The method 1000 includes, at 1004, generating and storing a file classifier using the n-gram vectors and the classification data as supervised training data. For example, the processor 111 executing the classifier generation instructions 115 may generate the file classifier 116 using the n-gram vectors and the classification data 103 as supervised training data 224. The file classifier 116 may be stored in the memory 112, may be provided to the second computing device 120, or both.

FIG. 11 is a flowchart illustrating another particular example of a method 1100 of using a file classifier. The method 1100 may be performed by the second computing device 120 of FIG. 1. For example, the processor 121 executing the classification application 124 from the memory 122 may perform the method 1100. In another implementation, the method 900 may be performed by the classification client application 154 in co-operation with the classification server application 170 of FIG. 2.

The method 1100 includes, at 1101, receiving one or more n-gram vectors for a file as input to a file classifier, where the one or more n-gram vectors indicates occurrences of groups of entropy indicators in a sequence of entropy indicators representing the file. For example, the processor 121 executing the classification application 124 may generate entropy indicators representing the file 128. To illustrate, the classification application 124 may include feature extraction instructions (such as the feature extraction instructions 113, the chunking instructions 401, the entropy calculation instructions 403, the entropy indicator generation instructions 405, or a combination thereof) that are configured to process binary files to generate the entropy indicators 410. Further, the classification application 124 may include vector generation instructions (such as the vector generation instructions 114) that are configured to generate the n-gram vectors for the multiple files and to provide the n-gram vectors as input to the file classifier 116

The method 1100 includes, at 1102, generating, based on the one or more n-gram vectors, output including classification data associated with the file, where the classification data indicate whether the file includes malware. For example, the processor 121 executing the classification application 124 may generate output including the classification result associated with the file 128.

FIG. 12 is a flowchart illustrating a particular example of a method 1200 of operation at the system 250 of FIG. 2B. In an illustrative aspect, the method 1200 may be performed by an enterprise server, such as one of the enterprise servers 255-257 of FIG. 2B.

The method 1200 includes receiving, at a server, a first file attribute from a computing device, at 1201. The first file attribute is associated with a file. For example, referring to FIG. 2C, the enterprise server 255 may receive the first file attribute 282 from the endpoint device 270, where the first file attribute 282 is associated with the file 280.

The method 1200 also includes determining, based on the first file attribute, that a classification for the file is unavailable, at 1202. For example, referring to FIGS. 2C and 2D, the enterprise server 255 may determine that a classification for the file 280 is unavailable in the local prediction cache 261 and may further determine (e.g., based on communication with the master servers 251) that the classification for the file is unavailable in the base prediction cache 253.

The method 1200 includes determining the classification for the file based on a trained file classification model accessible to the server, at 1203. For example, referring to FIG. 2E, the enterprise server 255 may classify the file 280 using the local file classification model 258. As another example, the master servers 251 may classify the file 280 using the base file classification model 252, such as in scenarios where the enterprise server 255 does not have a local file classification model. In illustrative examples, the file 280 is classified based on one a second file attribute, such as or more feature vectors determined from the file 280.

The method 1200 includes sending the classification (e.g., data indicating the classification) to the computing device, at 1204, and sending at least the classification (e.g., the data indicating the classification) to a base prediction cache associated with a second server, at 1205. For example, referring to FIG. 2E, the enterprise server 255 may send the classification 286 to the endpoint device 270 and to the base prediction cache 253. If the classification 286 indicates that the file 280 is malicious (or has at least a threshold probability of being malicious), the endpoint device 270 may delete the file 280, quarantine the file 280, display an on-screen alert, output an audible sound, or any combination thereof.

FIG. 13 is a flowchart illustrating another particular example of a method 1300 of operation at the system 250 of FIG. 2B. In an illustrative aspect, the method 1300 may be performed by one of the master servers 251 of FIG. 2B.

The method 1300 includes receiving, at a first server from a second server that is associated with a first group of computing devices, a first attribute of a first file and a classification for the first file, at 1301. For example, referring to FIGS. 2C-2E, the master server(s) 251 may receive from the enterprise server 255 the first file attribute 282 associated with the file 280 and the classification for the file 280.

The method 1300 includes adding the first file attribute and the classification to a base prediction cache, at 1302. For example, referring to FIG. 2E, the master server(s) 251 may add the first file attribute 282 and the classification 286 to the base prediction cache 253.

The method 1300 includes receiving, at the first server from a third server associated with a second group of computing devices, a second file attribute of a second file, at 1303. For example, referring to FIG. 2F, the master server(s) 251 may receive the file attribute 287 from the enterprise server 256, where the file attribute 287 is associated with the file 280 at an endpoint device of Enterprise 2.

The method 1300 includes determining, based on the base prediction cache, that the second file attribute matches the first file attribute, at 1304. For example, referring to FIG. 2F, the master server(s) 251 may determine that the file attributes 282 and 287 match (e.g., are the same SHA-256 values). Such matching file attributes indicate that the file 280 at the endpoint device of Enterprise 2 is the same as the file 280 previously encountered by the endpoint device 270 at Enterprise 1 in FIG. 2C.

The method 1300 includes, in response to determining that the second file attribute matches the first file attribute, sending, from the first server to the third server, the classification for the first file as a classification for the second file, at 1305. For example, referring to FIG. 2F, the master server(s) 251 may send the classification 286 to the enterprise server 256.

The systems and methods illustrated herein may be described in terms of functional block components, screen shots, optional selections and various processing steps. It should be appreciated that such functional blocks may be realized by any number of hardware and/or software components configured to perform the specified functions. For example, a system may employ various integrated circuit components, e.g., memory elements, processing elements, logic elements, look-up tables, and the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. Similarly, the software elements of the system may be implemented with any programming or scripting language such as C, C++, C#, Java, JavaScript, VBScript, Macromedia Cold Fusion, COBOL, Microsoft Active Server Pages, assembly, PERL, PHP, AWK, Python, Visual Basic, SQL Stored Procedures, PL/SQL, any UNIX shell script, and extensible markup language (XML) with the various algorithms being implemented with any combination of data structures, objects, processes, routines or other programming elements. Further, it should be noted that the system may employ any number of techniques for data transmission, signaling, data processing, network control, and the like.

The systems and methods of the present disclosure may be embodied as a customization of an existing system, an add-on product, a processing apparatus executing upgraded software, a standalone system, a distributed system, a method, a data processing system, a device for data processing, and/or a computer program product. Accordingly, any portion of the system or a module may take the form of a processing apparatus executing code, an internet based (e.g., cloud computing) embodiment, an entirely hardware embodiment, or an embodiment combining aspects of the internet, software and hardware. Furthermore, the system may take the form of a computer program product on a computer-readable (or processor-readable) storage medium or device having computer-readable (or processor-readable) program code (e.g., instructions) embodied or stored in the storage medium or device. Any suitable computer-readable (or processor-readable) storage medium or device may be utilized, including hard disks, CD-ROM, optical storage devices, magnetic storage devices, and/or other storage media. A computer-readable (or processor-readable) storage medium or device is not a signal.

Systems and methods may be described herein with reference to screen shots, block diagrams and flowchart illustrations of methods, apparatuses (e.g., systems), and computer media according to various aspects. It will be understood that each functional block of a block diagrams and flowchart illustration, and combinations of functional blocks in block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions.

Computer program instructions may be loaded onto a computer or other programmable data processing apparatus to produce a machine, such that the instructions that execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory or device that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each functional block of the block diagrams and flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, can be implemented by either special purpose hardware-based computer systems which perform the specified functions or steps, or suitable combinations of special purpose hardware and computer instructions.

Methods disclose herein may be embodied as computer program instructions on a tangible computer-readable medium, such as a magnetic or optical memory or a magnetic or optical disk/disc. All structural, chemical, and functional equivalents to the elements of the above-described exemplary embodiments that are known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the present claims. Moreover, it is not necessary for a device or method to address each and every problem sought to be solved by the present disclosure, for it to be encompassed by the present claims. Furthermore, no element, component, or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element, component, or method step is explicitly recited in the claims. As used herein, the terms “comprises”, “comprising”, or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Changes and modifications may be made to the disclosed embodiments without departing from the scope of the present disclosure. These and other changes or modifications are intended to be included within the scope of the present disclosure, as expressed in the following claims. 

What is claimed is:
 1. A processor-readable storage device storing instructions that, when executed, cause a processor to perform operations comprising: subsequent to determining, at a first device based on a first file attribute associated with a file, that a classification for the file is unavailable at the first device, sending the first file attribute from the first device to a second device to determine whether the classification for the file is available at the second device, the second device storing data corresponding to a plurality of file attributes analyzed by a plurality of devices; subsequent to sending the first file attribute to the second device, receiving a notification at the first device from the second device that the classification for the file is unavailable at the second device; in response to receiving the notification, determining the classification for the file by performing, at the first device, an analysis of a second file attribute based on a trained file classification model, wherein the second file attribute is associated with the file, and wherein the second file attribute is distinct from the first file attribute and distinct from an entirety of the file; sending the classification from the first device to the second device; storing the classification in a cache at the first device; and sending the classification to a third device responsive to determining that a third file attribute from the third device corresponds to the classification, wherein the classification is sent to the third device without sending the third file attribute to the second device subsequent to storing the classification.
 2. The processor-readable storage device of claim 1, wherein the classification includes a confidence level generated by the trained file classification model.
 3. The processor-readable storage device of claim 1, wherein the second device is coupled to a plurality of enterprise servers associated with a plurality of enterprises, each of the plurality of enterprise servers configured, in response to determining that a particular classification for a particular file is unavailable at a respective local prediction cache, to query the second device for the particular classification prior to executing a respective local trained classification model to determine the particular classification.
 4. The processor-readable storage device of claim 1, wherein the operations further comprise receiving the first file attribute from a fourth device.
 5. The processor-readable storage device of claim 1, wherein the first file attribute includes a secure hash algorithm (SHA) hash value determined from the file.
 6. The processor-readable storage device of claim 1, wherein the operations further comprise updating the trained file classification model based on a second classification model received from the second device, wherein the classification model includes data corresponding to the classification.
 7. The processor-readable storage device of claim 6, wherein the first device is associated with a first enterprise, and wherein the second classification model is distinct from a third classification model received at a fourth device associated with a second enterprise from the second device, and wherein the third classification model is trained on the classification.
 8. The processor-readable storage device of claim 1, wherein the second file attribute comprises at least one of: one or more n-gram vectors indicating occurrences of character pairs in printable characters representing content of the file, and a sequence of entropy indicators, each entropy indicator of the sequence of entropy indicators corresponding to a portion of the file.
 9. The processor-readable storage device of claim 1, wherein the second file attribute comprises an entropy indicator n-gram vector, the entropy indicator n-gram vector indicating occurrences of a plurality of n-grams in a sequence of entropy indicators representing the file, a first entropy indicator of the sequence corresponding to a first bin name associated with a first range of entropy values, a second entropy indicator of the sequence corresponding to a second bin name associated with a second range of entropy values, each of the plurality of n-grams comprising a combination of multiple bin names, wherein n is an integer greater than or equal to two.
 10. The processor-readable storage device of claim 1, wherein the operations further comprise receiving the third file attribute from the third device.
 11. A method comprising: subsequent to determining, at a first device based on a first file attribute associated with a file, that a classification for the file is unavailable at the first device, sending the first file attribute from the first device to a second device to determine whether the classification for the file is available at the second device; subsequent to sending the first file attribute to the second device, receiving the classification at the first device from the second device responsive to the classification for the file being available at the second device, wherein the second device stores data corresponding to a plurality of file attributes analyzed by a plurality of devices associated with the second device; storing the classification in a cache at the first device; and sending the classification to a third device responsive to determining that a second file attribute from the third device corresponds to the classification, wherein the classification is sent to the third device without sending the second file attribute to the second device subsequent to storing the classification.
 12. The method of claim 11, further comprising updating the trained file classification model based on the classification to generate an updated trained file classification model.
 13. The method of claim 11, wherein the first file attribute includes a secure hash algorithm (SHA) value determined from the file.
 14. The method of claim 11, wherein the second device is coupled to a plurality of enterprise servers associated with a plurality of enterprises, each of the plurality of enterprise servers configured, in response to determining that a particular classification for a particular file is unavailable at a respective local prediction cache, to query the second device for the particular classification prior to executing a respective local trained classification model to determine the particular classification.
 15. The method of claim 11, wherein the classification indicates a confidence level generated concurrently with the classification by a classification model.
 16. The method of claim 11, further comprising updating a classification model stored in the second device based on a second classification model received from the first device.
 17. A device comprising: a memory storing instructions; a first cache; and a processor configured to execute the instructions from the memory to perform operations including: subsequent to determining, based on a first file attribute associated with a file, that a classification for the file is unavailable at the device, sending the first file attribute from the device to a second device to determine whether the classification for the file is available at the second device; subsequent to sending the first file attribute to the second device, receiving the classification at the device from the second device responsive to the classification for the file being available at the second device, wherein the second device stores data corresponding to a plurality of file attributes analyzed by a plurality of devices associated with the second device; storing the classification in the first cache; and sending the classification to a third device responsive to determining that a third file attribute from the third device corresponds to the classification, wherein the classification is sent to the third device without sending the third file attribute to the second device subsequent to storing the classification.
 18. The device of claim 17, wherein the second device is coupled to a plurality of enterprise servers associated with a plurality of enterprises, each of the plurality of enterprise servers configured, in response to determining that a particular classification for a particular file is unavailable at a respective local prediction cache, to query the second device for the particular classification prior to executing a respective local trained classification model to determine the particular classification.
 19. The device of claim 17, wherein the first file attribute includes a secure hash algorithm (SHA) value.
 20. The device of claim 19, wherein the classification includes a confidence level generated concurrently with the classification by the trained file classification model. 